User blog:Homuhomu123/Estimation of Radar Modifiers on Artillery Spotting Chance
>> Due to LIMITED sample size and/or BAD experiment procedure, part of this article is considered obsolete. >> Click here to see the list of experiments performed by Homu Test. Objective of the Exp't This is my 3rd part of the ASTRE Project. Objectives of the exp't are: *Using LoS calibration method, to verify if all AA radars (eg. #14) provides a CI chance bonus of ~25%, which is 5% lower than the Surface radar modifier. *Using LoS calibration, verify the CI chance bonus from AP shells is +20%. *Using flagship status calibration, compare FuMO25(L) Anti-Air & #22-Kai 4(S) Surface [ Exp't E ] Information About the Exp't - Fleet & Test ship condition: *Morale <20 (severe fatigue / red face) *Always beyond moderate damage (中破) *Line ahead *AS+ achieved for every battle *No Saiun equipped - Enemy condition: *Either Line Ahead or DL. - Test ship & equipment: [ Exp't E ] - Completed in 2 Days (Total of 6h) - # Battles: 146 - Fleet total LOS = 405 -> 426 (+9% DA, +11.5% CI) - Non-S Results: 19 - Buckets used: 23 (2) - CV(L): Chitose KK2 lv 86->91 - Wildcard: Amatsukaze K. lv 55->63 Findings (Comparative Study) * (ASTRE - Comparative) '- DA ' *If we're allowed to calibrate the flagship modifier (+15% DA chance) and compare Chikuma w/ Tone, FuMO25 radar gives 1.5% more DA chance than #22-Kai 4 Surface. *If we're allowed to calibrate the FLoS difference between Exp't D & E (350 & 410), and compare Chikuma in the 2 exp'ts, we can see that #22-Kai 4 Surface gives 0.5% more DA chance than FuMO25. *According to the 2 ASTRE study above, we might say #22 Kai-4 (S) Surface & FuMO25 (L) gives the same DA chance bonus. -''' CI''' *If we're allowed to calibrate the FLoS difference between Exp't D & E (350 & 410), and compare Prinz Eugen in 2 the exp'ts, we can see that #14 (L) Anti-Air gives 7% lower DA chance than #22 (S) Surface. *For the same methodology we could compare Prinz Eugen in Exp't C & D, and FuMO25 (L) gives 5% lower DA chance than #22 (S) Surface. *According the 2 ASTRE study above, we may assume all surface radars give ~30% CI rate bonus, while all AA radars gives ~25%. This hypothesis will be further tested in future exp'ts.. Postscript & Intro to the Next Step [ Exp't F ] As the project proceeds, I gradually came to realize how estimates could be based on inaccurate assumptions, from which weak hypothesis might be made and used in further studies. As I tried to come up with formula which best fitted all the data, it is quite difficult to differentiate experiment errors and unindentified modifiers (A''lthough preliminary analysis did help a lot, from which future experiments could be designed). Deviations always exist, if not greater than 4% from my estimates. It is indeed not a large digit, but significant enough to alter most of my hypothesis (esp. those on DA chance). Therefore, from now on I will focus more on the repetability of conclusions, and external factors (artillery, ship class or type, etc) will be firmly controlled as much as I can. However, in compromise with feasibility, a few factors will NOT be studied in the near future, and assumed to have negligible effects as sample size gets larger (>160). Namely: #Minor damage modifier on DA/CI rate #Enemy formation #Engagement form And here is the list of uncertainty to be further studied (priority based): #Exact value for severe fatigue penalty on DA chance. Estimated to be at least 8%, could be slightly higher. #Relation between radar modifier on DA chance & their accuracy stat. ''Notably FuMO, #33 & #22. #Base DA rate for CA / CAV. Despite the convenience in calculation, it just seems that CA has 3% lower DA rate than CAV, as if CA only has a base rate of 47%. #Exact value for LoS modifier. Different interpretation & data leads to various modifier values. As a rough estimate, they are around +10% per 100 LoS, +/- 2% maximum. Next Step: *Epilogue : A brief reflection on Exp't A ~ E, and the use of standard error calculation. *[ Exp't F ] : Evaluation of CA/V Base Rate on Double Attack Illustration on How data were collected And wish everyone a merry Xmas! From Homuhomu Test. Category:Blog posts